LAPSE:2023.30834
Published Article
LAPSE:2023.30834
Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access
Cailian Gu, Yibo Wang, Weisheng Wang, Yang Gao
April 17, 2023
Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms of power supply and load in distribution networks are analyzed, the dynamic model of distribution networks based on complex network theory is established, and the risk index of voltage exceeding limits under the conditions of high permeability distributed power supply access is put forward. Secondly, the random power flow of distribution networks based on the Monte Carlo method is studied by sampling and analyzing the dynamic model of distribution networks. Then, the risk calculation and safety assessment of voltage exceeding limits are carried out on the currently extracted model, and the risk control strategy of distribution network operation is put forward. Finally, an improved IEEE30-node distribution network topology is proposed. Through simulation analysis, it is proven that the load situation awareness early warning method of distribution networks can effectively predict, improve the security of distribution networks, and provide timely early warning information for maintenance personnel.
Keywords
distributed power supply, load flow analysis, new energy, power system
Suggested Citation
Gu C, Wang Y, Wang W, Gao Y. Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access. (2023). LAPSE:2023.30834
Author Affiliations
Gu C: Shenyang Institute of Engineering, Shenyang 110136, China
Wang Y: College of Information Science and Engineering, Northeastern University, Shenyang 314001, China
Wang W: College of Information Science and Engineering, Northeastern University, Shenyang 314001, China
Gao Y: Shenyang Institute of Engineering, Shenyang 110136, China
Journal Name
Energies
Volume
16
Issue
7
First Page
3093
Year
2023
Publication Date
2023-03-28
Published Version
ISSN
1996-1073
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PII: en16073093, Publication Type: Journal Article
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LAPSE:2023.30834
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doi:10.3390/en16073093
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